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- James Cheng, Yiping Ke, Wilfred Ng, An Lu
- SIGMOD Conference
- 2007

Graphs are prevalently used to model the relationships between objects in various domains. With the increasing usage of graph databases, it has become more and more demanding to efficiently process graph queries. Querying graph databases is costly since it involves <i>subgraph isomorphism</i> testing, which is an <i>NP-complete</i> problem. In recent years,… (More)

- James Cheng, Ada Wai-Chee Fu, Jia Liu
- SIGMOD Conference
- 2010

Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due to the diversity and complexity of graph data, on which an adversary can use many types of background knowledge to conduct an attack. One popular type of attacks as studied by… (More)

- Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, James Cheng
- SIGMOD Conference
- 2012

Graph clustering, also known as community detection, is a long-standing problem in data mining. However, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage structural and attribute information for clustering attributed graphs becomes a new challenge. Most existing works take a distance-based… (More)

- Jia Wang, James Cheng
- PVLDB
- 2012

The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the “core” of a k-core that keeps the key information of, while… (More)

- Da Yan, James Cheng, Yi Lu, Wilfred Ng
- PVLDB
- 2014

The rapid growth in the volume of many real-world graphs (e.g., social networks, web graphs, and spatial networks) has led to the development of various vertex-centric distributed graph computing systems in recent years. However, real-world graphs from different domains have very different characteristics, which often create bottlenecks in vertex-centric… (More)

- James Cheng, Wilfred Ng
- EDBT
- 2004

XML makes data flexible in representation and easily portable on the Web but it also substantially inflates data size as a consequence of using tags to describe data. Although many effective XML compressors, such as XMill, have been recently proposed to solve this data inflation problem, they do not address the problem of running queries on compressed XML… (More)

- Shumo Chu, James Cheng
- KDD
- 2011

Triangle listing is one of the fundamental algorithmic problems whose solution has numerous applications especially in the analysis of complex networks, such as the computation of clustering coefficient, transitivity, triangular connectivity, etc. Existing algorithms for triangle listing are mainly in-memory algorithms, whose performance cannot scale with… (More)

- James Cheng, Silu Huang, Huanhuan Wu, Ada Wai-Chee Fu
- SIGMOD Conference
- 2013

Reachability querying is a basic graph operation with numerous important applications in databases, network analysis, computational biology, software engineering, etc. Although many indexes have been proposed to answer reachability queries, most of them are only efficient for handling relatively small graphs. We propose TF-label, an efficient and scalable… (More)

- James Cheng, Yiping Ke, Shumo Chu, M. Tamer Özsu
- 2011 IEEE 27th International Conference on Data…
- 2011

The k-core of a graph is the largest subgraph in which every vertex is connected to at least k other vertices within the subgraph. Core decomposition finds the k-core of the graph for every possible k. Past studies have shown important applications of core decomposition such as in the study of the properties of large networks (e.g., sustainability,… (More)

- James Cheng, Yiping Ke, Wilfred Ng
- Knowledge and Information Systems
- 2007

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the… (More)